Incomplete-data CT image reconstructions in industrial applications

In industrial X-ray computerized tomography (CT), the objects to be inspected are usually very attenuating to X-rays, and their shape may not permit complete scannings at all view angles; incomplete-data imaging situations usually result. In earlier reports, it was concluded that image reconstruction from incomplete data can be achieved effectively through an iterative transform algorithm, which utilizes the a priori information on the object to compensate for the missing data. The image is repeatedly transformed to the object space, where it is corrected by the a priori information on the object, and back to the projection space where it is corrected by the measured projections. The results of validating the iterative transform algorithm on experimental data from a cross section of a high-pressure turbine blade made of Ni-based superalloy are reported. From the data set, two kinds of incomplete data situations are simulated: incomplete projection and limited-angle scanning. The results indicate that substantial improvements, both visually and in wall thickness measurements, were brought about in all cases through the use of the iterative transform algorithm